Name
Title | ||
|---|---|---|
On The Distribution Of Salient Objects In Web Images And Its Influence On Salient Object Detection |
Abstract | ||
|---|---|---|
In recent years it has become apparent that a Gaussian center bias can serve as an important prior for visual saliency detection, which has been demonstrated for predicting human eye fixations and salient object detection. Tseng et al. have shown that the photographer's tendency to place interesting objects in the center is a likely cause for the center bias of eye fixations. We investigate the influence of the photographer's center bias on salient object detection, extending our previous work. We show that the centroid locations of salient objects in photographs of Achanta and Liu's data set in fact correlate strongly with a Gaussian model. This is an important insight, because it provides an empirical motivation and justification for the integration of such a center bias in salient object detection algorithms and helps to understand why Gaussian models are so effective. To assess the influence of the center bias on salient object detection, we integrate an explicit Gaussian center bias model into two state-of-the-art salient object detection algorithms. This way, first, we quantify the influence of the Gaussian center bias on pixel- and segment-based salient object detection. Second, we improve the performance in terms of F-1 score, F-beta score, area under the recall-precision curve, area under the receiver operating characteristic curve, and hit-rate on the well-known data set by Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we exemplarily demonstrate that implicit center biases are partially responsible for the outstanding performance of state-of-the-art algorithms. Last but not least, we introduce a non-biased salient object detection method, which is of interest for applications in which the image data is not likely to have a photographer's center bias (e.g., image data of surveillance cameras or autonomous robots). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1371/journal.pone.0130316 | PLOS ONE |
Keywords | Field | DocType |
biology,chemistry,medicine,physics,engineering | F1 score,Computer vision,Debiasing,Fixation (psychology),Gaussian,Artificial intelligence,Gaussian network model,Pixel,Visual perception,Centroid,Physics | Journal |
Volume | Issue | ISSN |
10 | 7 | 1932-6203 |
Citations | PageRank | References |
1 | 0.35 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Schauerte | 1 | 6 | 1.08 |
| Rainer Stiefelhagen | 2 | 1 | 0.35 |